US11638563B2 - Predictive fall event management system and method of using same - Google Patents

Predictive fall event management system and method of using same Download PDF

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US11638563B2
US11638563B2 US16/725,766 US201916725766A US11638563B2 US 11638563 B2 US11638563 B2 US 11638563B2 US 201916725766 A US201916725766 A US 201916725766A US 11638563 B2 US11638563 B2 US 11638563B2
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fall
future
physiological
data
contextual
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US20200205746A1 (en
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Justin R. Burwinkel
Buye Xu
Jason A. Galster
Peter J. Tetrick
Sourav K. Bhunia
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Starkey Laboratories Inc
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Starkey Laboratories Inc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/112—Gait analysis
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0446—Sensor means for detecting worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • Maintaining postural control and preventing a fall are of importance for the elderly. Falls are the second leading cause of accidental or unintentional injury deaths worldwide and are especially prevalent in the elderly. Currently, individuals are often inadequately prepared to protect themselves from falls or other serious injuries as the onset of such events can come without perceptible warning. Further, maintaining postural equilibrium, i.e., prevention of a fall, involves stabilization of the body's center of mass during both self-initiated and externally triggered disturbances to postural stability during normal daily activities. Maintaining such equilibrium can be accomplished by limiting the motion of the center of mass within the base of support formed by and around the feet. Postural equilibrium is maintained through multisensory inputs.
  • loss of sensory input in the feet due to neuropathy can increase the risk of a fall, even though the necessary motor control for a corrective action of repositioning the feet can still be intact.
  • low vision or reduced ranges of hearing can prevent an individual from detecting hazards within their environment such that they can avoid them.
  • the present disclosure provides various embodiments of a predictive fall event management system and a method of utilizing such system.
  • the system evaluates future risk conditions that can be used to predict the occurrence of fall events.
  • the system can include a body-worn device and a controller operatively connected to the body-worn device.
  • the controller can be adapted to determine, for a future time, whether a fall condition is satisfied based upon one or more future physiological states or contextual states of a wearer of the body-worn device.
  • Such future physiological states and contextual states can be determined based upon one or more physiological data and contextual data related to the wearer.
  • the present disclosure provides a predictive fall event management system that includes a body-worn device and a controller operatively connected to the body-worn device.
  • the controller is adapted to receive physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period, receive contextual data representative of context information of the wearer over the monitoring time period, and determine one or more future physiological states or contextual states based at least in part on one or more of the physiological data and the contextual data.
  • the controller is further adapted to determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states and generate a fall prevention output responsive to satisfaction of the fall condition.
  • the present disclosure provides a method that includes receiving physiological data representative of a physiological characteristic of a wearer of a body-worn device over a monitoring time period, receiving contextual data representative of context information of the wearer over the monitoring time period, and determining one or more future physiological states or contextual states based at least in part on one or more of the physiological data or the contextual data.
  • the method further includes determining, for a future time, whether a fall condition is satisfied based upon one of the one or more future physiological states or contextual states and generating a fall prevention output responsive to satisfaction of the fall condition.
  • FIG. 1 A is a graph of frequency of a fall versus time for an individual.
  • FIG. 1 B is a graph of standard predicted probability of a fall versus time for the individual of FIG. 1 A .
  • FIG. 1 C is a graph of predicted probability of a fall versus time for the individual of FIG. 1 A .
  • FIGS. 2 A-C are graphs of weighted risk factors for a fall versus time, wherein FIG. 2 A is a graph of balance risk factors based upon a screening test versus time for an individual, FIG. 2 B is a graph of a medication's risk factors versus time for the individual, and FIG. 2 C is a graph of risk factors associated with the individual moving from a seated to a standing position versus time.
  • FIGS. 3 A-B are graphs of weighted overall risk factors of a fall for the individual of FIGS. 2 A-C , where FIG. 3 A is a graph of an overall risk of a fall versus time for the individual one hour after the individual ingests a particular medication, and FIG. 3 B is a graph of the overall risk of a fall versus time for the individual seven hours after ingestion of the medication.
  • FIG. 4 is a schematic diagram of one embodiment of a predictive fall event management system.
  • FIG. 5 is a schematic diagram of a body-worn device of the system of FIG. 4 .
  • FIG. 6 is a flowchart of one embodiment of a method that can be utilized with the predictive fall event management system.
  • the present disclosure provides various embodiments of a predictive fall event management system and a method of utilizing such system.
  • the system can include a body-worn device and a controller operatively connected to the body-worn device.
  • the controller can be adapted to determine, for a future time, whether a fall condition is satisfied based upon one or more future physiological states or contextual states of a wearer of the body-worn device.
  • Such future physiological states and contextual states can be determined based upon one or more physiological data and contextual data related to the wearer.
  • Real-world fall risk factors that can lead to a fall by an individual can be dynamic and not constant as they can change over time and are highly individualized.
  • Current methods of assessing the individual's risk for falling do not account for these complex, real-world dynamics.
  • clinical techniques for assessing an individual's long-term falls risk level are performed in a hospital or doctor's office. Generally, these tests are not assessed frequently enough to track abrupt changes in risk level, such as when an individual has changed her medications. Moreover, these clinical tests may not be sensitive enough to monitor gradual declines and improvements that might occur moment to moment.
  • Other techniques that can be utilized remote from a clinical setting can detect the onset and early stages of falls, in some cases prior to the individual impacting the ground.
  • a near-term prediction system that can account for both an individual's long-term risk level and momentary risk level is desirable.
  • This type of predictive fall event management system would inform a user of momentarily-elevated risks levels or take automated actions, on the user's behalf, to reduce her risk of falling during her daily activity.
  • Customized interventions for near-term risk factor mitigation can also be informed by knowledge of the user's long-term risk factors.
  • One or more embodiments of the present disclosure provides a predictive fall event management system that can utilize time-varying physiological and contextual factors. Instead of only weighing a user's various risk factors, the system can adapt to physiological and contextual information related to the user and employ individualized learning that can modify the system over time. In one or more embodiments, the system can apply different weights to the various fall risk factors depending upon their influence on one or more fall risk levels and the recency and certainty of available contextual information such as the user's environment, schedule, medical history, etc. Over varying periods of time, values or statistics representing fall risk factors can either be extinguished or maximized by the system, unless these risk factor estimations continue to be supported by more recent data.
  • the recency of input data can affect the statistics, e.g., the confidence intervals relative to the risk factors assessed using the input data.
  • Risk factor weightings can also have condition-specific dependencies that can influence the extinction and maximization of the fall risk level estimations.
  • the strength or severity of condition-specific dependencies and associations can affect the statistics, e.g., the confidence intervals relative to the risk factors assessed using the input data.
  • a near-term fall prediction can anticipate a fall prior to the individual losing her balance, from which she may not be able to recover without falling.
  • a near-term falls prediction system can be useful for accurately informing a user of momentarily elevated risks levels and taking automated actions on the user's behalf during daily activity.
  • the taxonomy of fall risk can be broken down into many risk factors. Some factors can affect the user for a long period of time (i.e., long-term risk factors such as age-related balance system deterioration), while others may affect the user over a relatively short period of time (i.e., short-term risk factors such as a decrease of oxygen level in the brain due to sudden movement). At any given time, a risk factor may or may not impose real fall risks because the factor also depends on the conditions (i.e., states or contexts) that the user may be experiencing.
  • long-term risk factors such as age-related balance system deterioration
  • short-term risk factors such as a decrease of oxygen level in the brain due to sudden movement
  • an individual can have an elevated risk level of a fall due to balance problems resulting from a knee injury.
  • This individual's primary falls risk factor does not put the individual at real risk for falling while the individual is stationary (e.g. standing, sitting or lying), but the risk level can increase once the individual starts to walk.
  • the risk of falling may be further increased if the individual increases the walking speed and/or walks on a slippery surface.
  • falls risk factors There are numerous falls risk factors that can be measured or inferred for a user.
  • falls risk factors can be characterized as intrinsic or extrinsic. Risk factors for falling, however, rarely remain constant over time.
  • the contributions of falls risk factors to the real risk of a fall also depend on the states, contexts and conditions that a patient is in (e.g., postural position, activity, environment, alertness, etc.). Fall risks have traditionally only been assessed in terms of a general, long-term fall risk level.
  • Other systems namely those that include airbags that are intended to reduce the physical impact of a fall on an elderly person's hips, have been able to detect the onset of falls prior to the individual reaching the ground.
  • FIGS. 1 A-C The frequency of actual falls versus both the state-of-the-art and one or more embodiments of fall event management systems described herein can be contrasted graphically as shown in FIGS. 1 A-C .
  • FIG. 1 A an individual experiences actual falls at times t 1 , t 2 , t 3 , and t 4 .
  • a current risk prediction model which is illustrated by curve 10 of FIG. 1 B , provide a constant probability of a fall over a period of time (i.e., a monitoring time period).
  • One or more embodiments of a predictive fall event management systems of the present disclosure can provide a system that incorporates time-dependent and time-independent fall risk factors using techniques that can be time dependent or independent as schematically illustrated by curve 12 of FIG. 1 C .
  • the present disclosure can provide one or more embodiments of a predictive fall event management system that utilizes time-varying risk and context factors. Instead of weighing an individual's different risk factors, one or more embodiments of a system described herein, that can adapt to physiological and contextual information related to a user and utilize individualized learning, can provide one or more predictions of the potential that the user may fall at a given point in the future.
  • the system applies different weights to the various fall risk factors depending upon their influence on a falls risk level and the recency and certainty of the available contextual information. After varying lengths of time, either the values or statistics or the contributions of various risk factors may be extinguished or maximized in a particular user's fall event management system.
  • a fall event in a unit time interval (UTI)
  • UTI unit time interval
  • the length of the UTI depends on the time taken for an individual to recover to an upright posture after a fall event, assuming no significant consequences to the user were caused by the fall such as a broken limb or unconsciousness.
  • the value of UTI can vary from a few seconds to a few minutes. Some people may need longer recovery time than others (e.g. elderly individuals may take a longer time to stand up after falling down than young healthy individuals) or, indeed, some individuals may actually be at risk for falling again as they attempt to stand up after a fall and require a shorter UTI.
  • the UTI can sometimes be extended to hours, days, or even weeks or months, until the occurrence of certain events such as a fall or when time-sensitive risk factors occur.
  • the disclosed fall risk model can be written as the sum of many sub-models: ⁇ circumflex over (p) ⁇ ( t n ) ⁇ M 1 ( r 1
  • orthostatic hypotension can be a binary risk factor based on a medical history screening (i.e., yes or no), or it can be a measured risk factor (i.e., an observed decrease in blood pressure after standing up).
  • these two inputs would not offer substantially different information from each other, so for some embodiments their weightings in the predictive fall event management system can be collapsed into a singular weighting or as individual factors with an otherwise reduced weighting when factored separately. That said, the measured variant can make the risk factor more time- or context-specific, and thus more predictive and deserving of a higher weighting value than that of the binary risk factor that could have been reported days, weeks, months, or even years earlier.
  • the medical history risk factor can serve to validate a measured risk factor, and the applied weighting can be adapted since it is somewhat based upon a clinical observation.
  • measures of physical strength like handheld myometry, might overlap with other forms of physical strength measurement such as the sit-to-stand test or TUG test or the like. Characteristics of these clinical tests can also be measured during ambulatory activities, but similar elements contributing to the risk estimation, stemming from a variety of inputs, can be prevented from biasing the predictive fall event management system by identifying or assigning duplicated factor values or statistics in the equation of a sub-model.
  • a complex and adaptive risk factors classification and weightings database can be maintained for the system to reference.
  • One or more sub-models of the predictive fall event management system can be derived based on known principles and/or trained based on learned data. Depending upon the nature of the risk factors, various machine learning models can be applied to generate the sub-models (e.g., linear regression, decision tree, Bayesian models, neural network, Kalman filtering, etc.). The confidence intervals of a prediction by each sub-model can also be computed and compared to an overall confidence interval for the overall prediction of the system.
  • One or more embodiments of the disclosed predictive fall event management system can predict the probability of falling at a future time (i.e., a fall condition) beyond the immediate UTI, i.e., over a monitoring time period, which can be enabled by incorporating the time varying risk factor values and conditions r i and c i .
  • the term “fall condition” refers to a condition where a wearer of the body-worn device is at risk for experiencing a fall, e.g., where a probability of a fall crosses a threshold, or a model indicates that a fall has occurred or can occur in a state having similarities to a predicted state.
  • a fall condition can also be or include a predicted or potential outcome of a fall, e.g., a likelihood of injury or a magnitude of an injury. It will be appreciated that the “probability of a fall” can include various other statistical metrics, such as a confidence interval or value related to the calculated “probability of a fall.”
  • FIGS. 2 A-C are graphs of weighted fall risk values or factors versus time for a user that ingests a medication that causes dizziness.
  • dizziness often peaks at thirty minutes after ingestion and then dissipates over a period of, e.g., about four hours as is illustrated by curve 16 of FIG. 2 B .
  • the system can also evaluate other fall risk values or factors such as the particular user's historical balance risk value as shown by curve 14 in FIG. 2 A and the user's historical balance data for when the user stands up from a seated position as illustrated by curve 18 in FIG. 2 C .
  • the output of the system can predict whether a fall condition is satisfied around the time that dizziness or blurred vision is projected to peak as shown by curve 20 of FIG. 3 A , which is the weighted composite fall risk value for the user one hour after ingestion of the medication, and curve 22 of FIG. 3 B , which is the weighted composite fall risk value seven hours after ingestion of the medication.
  • the level of dizziness can be updated using, e.g., Kalman filtering approaches, in real time, if informative physiological information can be measured or if relevant questionnaires can be administered.
  • the fall risk can be recomputed as new information is added.
  • the historical data relating to dizziness level or imbalance, associated with previous occurrences of taking the medicine can be used to customize the medicine's effects on the user, thereby improving the accuracy of the individual's fall risk estimation.
  • historical data relating to dizziness level or imbalance, associated with previous occurrences of taking the medicine can be used to assist the user or a medical professional in optimizing the times at which the user takes their medications such that the user's risk for falling would be minimized when the user is predicted to be ambulatory or otherwise exposed to conditions or contexts that would elevate the individual's risk for falling.
  • one or more embodiments of the predictive fall event management system can infer that the user will be walking to an appointment within the next hour.
  • the sidewalks on which the user will be traversing to get to the appointment have been reported, by other users, weather predictions, or hazard beacons, to be slippery due to icy conditions.
  • these sidewalks can be assigned a high value as an extrinsic falls risk factor for users until four hours later when the temperature is expected to become sufficiently warm, long enough to melt the ice.
  • the related fall risk sub-model can predict a sharp increase of a fall risk in one hour.
  • the overall fall risk predicted by the system can satisfy a fall condition, thereby triggering one or more fall prevention outputs such as warnings well ahead of the predicted fall so that the user can react accordingly.
  • the user can optionally be prompted with the risk estimate and then asked about their planned method of travel.
  • the user can be directed to call for a driver or to contact a caregiver to assist them in making it to the appointment safely.
  • these tasks can be initiated or otherwise undertaken upon the user's behalf by an electronic system or an artificial intelligence assistant.
  • the physiological factor models and contextual factor models can be adapted to each individual user, over time, by comparing the model prediction and any present observations that are available. These adaptations can also be informed by data collected from individuals or groups of individuals other than the user. Classification and weighting of each risk factor, which can be stored in one or more databases, can assist in assigning values to the risk factor based upon the severity of the condition (i.e., risk factor scaling), its known predictive value, statistics, (e.g., confidence intervals), recency of the input data, and the novelty of information that each input provides into the user's falls risk determination.
  • severity of the condition i.e., risk factor scaling
  • statistics e.g., confidence intervals
  • These databases can also have values or statistics and offsets that account for the fact that not every possible input available for calculation into the predictive fall event management system will have been measured or entered into the system for that individual. This may change over time as more information is collected (or expires) regarding the individual user, so the database can provide dynamic values or statistics based upon which inputs are being factored into any given user's risk estimation at any given time. Changes in risk factor weightings over time can also be stored as “time coefficients” within the risk factors classification and weightings database.
  • Conditions or thresholds can be set for individual composite risk estimation values, the corresponding time in the future, and their respective statistics, e.g., the size of the confidence intervals. These conditions may be stepped in any appropriate manner to indicate degrees of severity (e.g., high, medium, low), and different actions and interventions may be triggered at different condition levels. Further, data trends can be stored and reviewed for comparison over time. A count of how frequently conditions are satisfied can also be maintained in memory. These data may be accessed by the user or other interested parties and for machine learning that can be adapted to modify the predictive fall event management system.
  • the user might be warned before encountering the icy street such that the user can, e.g., change routes, alter their means of travel, or reschedule the appointment.
  • the types of actions and interventions from the system can vary for different situations.
  • the system can be adapted to generate a fall prevention output such as instructing the user to wait for assistance and informing other individuals who may be able to provide that assistance to the user.
  • the system can be adapted to generate a fall prevention output that informs the user that she may feel lightheaded or dizzy and instructs her to wait a few moments before beginning to walk.
  • the fall prevention output may be directed at controlling an operatively connected assistive device or exoskeleton.
  • FIG. 4 is a schematic diagram of one embodiment of a predictive fall event management system 100 .
  • the system 100 includes a body-worn device 102 for a wearer, an optional sensor 104 operatively connected to the body-worn device, and a controller 106 operatively connected to one or both of the body-worn device and the controller.
  • the term “operatively connected” means that an element or component can be connected to another element or component using any suitable technique or techniques such that information can be shared between such components.
  • the sensor 104 can be operatively connected to the body-worn device 102 by a wire or cable, wirelessly using any suitable wireless protocol, optically, over the internet, mesh network, etc.
  • the controller 106 is adapted to receive physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period, receive contextual data representative of context information of the wearer over the monitoring time period, and determine one or more future physiological states or contextual states based at least in part on one or more of the physiological data and the contextual data.
  • physiological data refers information regarding the wearer's physiological state, e.g., at least one of a determined fall risk, inertial sensor data, heart rate information, blood pressure information, drug concentration information, blood sugar level, body hydration information, neuropathy information, blood oximetry information, hematocrit information, cortisol level, body temperature, age, sex, gait or postural stability attribute, vision, hearing, eye movement, neurological activity, head movement, or the like.
  • physiological data can include psychological data representative of a psychological state such as a fear of falling. Such psychological state can, in one or more embodiments, be detected from physiological data or combinations of physiological data such as heart rate, core body temperature, or cortisol levels.
  • the physiological data can include, in part or in combination, one or more inputs provided by the wearer in response to one or more queries.
  • contextual data refers to data representative of a context within which the user is disposed or will be disposed at a future time.
  • contextual data can include at least one of weather condition, environmental condition, sensed condition, location, velocity, acceleration, direction, hazard beacon, type of establishment occupied by the wearer, camera information, or presence of stairs, etc.
  • hazard beacons can provide contextual data to the system 100 .
  • Such hazard beacons can include physical or virtual beacons as described, e.g., in U.S. Patent Publication No. 2018/0233018 A1, entitled FALL PREDICTION SYSTEM INCLUDING A BEACON AND METHOD OF USING SAME.
  • the term “future physiological state” means values or statistics relative to a physiological state that is predicted to occur at a future time, where such statistics can include one or more of a probability, confidence interval, distribution, range, or the like.
  • the term “future contextual state” means values or statistics relative to a contextual state that is predicted to occur at a future time, where such statistics can include one or more of a probability, confidence interval, distribution, range, or the like.
  • the future time of the contextual data and/or future physiological state can vary.
  • the future time can be about 5 seconds, 10 seconds, 30 seconds, 60 seconds, 2 minutes, 5 minutes, 10 minutes, 20 minutes, 30 minutes, 45 minutes, 60 minutes, 90 minutes, 120 minutes, 180 minutes, 240 minutes, 300 minutes, 12 hours, 24 hours, 48 hours, 72 hours, 1 week or longer, or an amount of time falling within a range between any of the foregoing.
  • the controller 106 is also adapted to determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states and generate a fall prevention output responsive to satisfaction of the fall condition.
  • the term “fall condition” refers to a condition where a wearer of the body-worn device is at risk for experiencing a fall, e.g., where a probability of a fall crosses a threshold, or a model indicates that a fall, near-fall, or balance event has occurred or may occur in a state having similarities to a predicted state.
  • a fall condition may also be or include a predicted or potential outcome of a fall, e.g., a likelihood of injury or a predicted magnitude of an injury.
  • the future time for determination of whether a fall condition is satisfied can vary.
  • the future time can be about 5 seconds, 10 seconds, 30 seconds, 60 seconds, 2 minutes, 5 minutes, 10 minutes, 20 minutes, 30 minutes, 45 minutes, 60 minutes, 90 minutes, 120 minutes, 180 minutes, 240 minutes, 300 minutes, 12 hours, 24 hours, 48 hours, 72 hours, 1 week or longer, or an amount of time falling within a range between any of the foregoing.
  • the controller 106 can be further adapted to determine, for a future time, whether the fall condition is satisfied by determining a future fall risk value based upon the one or more future physiological states or contextual states.
  • the term “future fall risk value” means value or statistics relative to a fall risk value that is predicted to occur at a future time, where such statistics can include one or more of a probability, confidence interval, distribution, range, or the like.
  • the controller 106 can further be adapted to receive balance data representative of a balance event and update the fall risk model based upon one or more of the physiological data, contextual data, and the balance data.
  • the controller 106 can be adapted generate a fall prevention output responsive to satisfaction of the fall condition.
  • Any suitable fall prevention output can be provided that can assist in mitigating the risk of the user falling, e.g., one or more of an audible alert, visual alert, or tactile alert provided to the wearer.
  • the user can be warned of the user acting on a predicted behavior when the predicted risk associated with the predicted behavior crosses a threshold for alerting the user. Thresholds for alerting the user and/or undertaking other fall prevention outputs can vary.
  • the thresholds can be specified in terms of a probability of a fall occurring and can be about 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 98, or 99 percent, or an amount falling within a range between any of the foregoing.
  • the specific fall prevention output undertaken can vary based on the probability of the fall occurring, with more aggressive fall prevention outputs being taken when the probability of a fall occurring is higher.
  • the thresholds can be preset when the system is manufactured, can be set/reset if the system determines that the user's (device wearer's) long-term risk for falling has changed, can be set by a care provider or clinician, can be set by the device wearer, or the like. In some cases the thresholds can be static and in other cases the thresholds can be dynamic.
  • the user can be advised of suitable options or alternatives to acting on a particular predicted behavior to protect the user from engaging in unnecessary risky behaviors.
  • the user can be given directions or advice to mitigate their risks.
  • the use of virtual reality or augmented reality such as those described, e.g., in U.S. Patent Publication No. 2018/0233018A1, entitled FALL PREDICTION SYSTEM INCLUDING A BEACON AND METHOD OF USING SAME can be used to assist the user in avoiding potential risks.
  • the fall prevention output can include electrical stimulation and/or thermal stimulation. Aspects of stimulation and other outputs are described in U.S. Pat. Appl. No. 62/873,598 entitled “SYSTEMS AND DEVICES FOR TREATING EQUILIBRIUM DISORDERS AND IMPROVING GAIT AND BALANCE”, the content of which is herein incorporated by reference.
  • the fall prevention output can include initiating a mitigation therapy or corrective measures e.g., a balance training regimen.
  • the fall prevention output can further include controlling or modifying one or more features and actions of a mobility device, an assistive device, or an exoskeleton.
  • the fall prevention output can include one or more modifications of an environmental context of the wearer. Any suitable environmental contexts can be modified to satisfaction of a predicted fall condition.
  • the fall prevention output can include modifying, via mesh networks or the internet of things (IoT), e.g., the lighting within an area proximate to the user or within an area where the user is predicted to be.
  • IoT internet of things
  • the fall prevention output can include modifying the temperature, oxygen mix, humidity, or air quality of an area proximate to the user or within an area where the user is predicted to be. Further, in one or more embodiments, the fall prevention output can include transmission of one or more of the physiological data and the contextual data to one or more of a caregiver, a medical professional, a database, or the wearer.
  • the fall prevention output can also include adapting one or more of a fall detection system setting such that the fall detection system is more likely to indicate that a fall has occurred if the predicted fall risk statistics crossed a certain threshold leading up to the occurrence in question as described, e.g., as described in U.S. patent application Ser. No. 16/714,339, entitled “HEARING ASSISTANCE SYSTEM WITH ENHANCED FALL DETECTION FEATURES”.
  • the adaption of fall detection system settings can be applied transitorily such that the system is only more likely to indicate that a fall has occurred for a period of seconds or minutes. In one or more embodiments, the adaption of fall detection system settings can be applied over a longer period of time.
  • the controller 106 can be further adapted to determine, for each of a plurality of future times, whether an anticipated fall condition is satisfied based upon one or more of the physiological data and the contextual data. Further, in one or more embodiments, the controller 106 can further be adapted to determine, for a plurality of future times, whether an anticipated fall condition is satisfied based upon a combination of the physiological data and the contextual data.
  • the controller 106 can further be adapted to detect disruption to the wearer's postural stability or confidence.
  • the controller 106 can detect a vestibular disturbance of the wearer, an occurrence of a fall, an episode of dizziness, an episode of syncope, a seizure, a stroke, an anaphylactic shock, an aneurysm, a hard sit into a chair, a near fall, a trip, a stumble, a change in gait, a hesitation or a fear of falling, or the like.
  • the optional sensor 104 is adapted to detect a physiological characteristic of the wearer of the body-worn device 102 and generate physiological data representative of the physiological characteristic. Further, the controller is adapted to receive the physiological data from the sensor 104 .
  • the system 100 can include any suitable body-worn device 102 for the wearer that can be worn in any suitable location on the body of the wearer, including but not limited to, a wearable hearing device such as headphones, a wrist-worn device such as a smartwatch, a patch disposed on any portion of a body of the wearer, glasses, etc.
  • the body-worn device can be implanted.
  • the body-worn device can, in one or more embodiments, be integrated into or otherwise disposed in a body part prosthesis, mobility assistance device, or exoskeleton.
  • the device 102 can instead be disposed apart from the body of the wearer.
  • the device 102 can be disposed in a room or building and be adapted to monitor one or more individuals proximate to the device using any suitable technique or techniques.
  • the device 102 can utilize at least one of lidar, facial recognition software, camera systems, hazard beacons, Bluetooth-based directional arrival software, geotagging, radio frequency identification (RFID), light sensors and the like to identify and track an individual located in the room or building and provide at least one of physiological data or contextual data regarding the individual to the controller 106 for further processing as is described herein.
  • RFID radio frequency identification
  • the body-worn device 102 can include a hearing assistance device such as behind-the-ear (BTE), in-the-ear (ITE), in-the-canal (ITC), or completely-in-the-canal (CIC) type hearing instrument.
  • BTE behind-the-ear
  • ITE in-the-ear
  • ITC in-the-canal
  • CIC completely-in-the-canal
  • hearing assistance device such as behind-the-ear (BTE), in-the-ear (ITE), in-the-canal (ITC), or completely-in-the-canal (CIC) type hearing instrument.
  • BTE behind-the-ear
  • ITE in-the-ear
  • ITC in-the-canal
  • CIC completely-in-the-canal
  • hearing assistance device such as behind-the-ear (BTE), in-the-ear (ITE), in-the-canal (ITC), or completely-in-the-canal (CIC) type hearing instrument.
  • BTE behind-the-ear
  • the body-worn device 102 can include a cochlear implant (including its processor) or a bone-conduction or otherwise osseointegrated hearing device. It is understood that other body-worn devices not expressly stated herein can fall within the scope of the present subject matter. While depicted as including one body-worn device 102 , the system 100 can include two or more body-worn devices. For example, in one or more embodiments, the system 100 can include a left hearing device that is adapted to be acoustically connected to the wearer's left ear and a right hearing device that is adapted to be acoustically connected to the wearer's right ear. In one or more embodiments, the left hearing device can electrically communicate with the right hearing device using any suitable technique or techniques.
  • the body-worn device 102 can include any suitable electronic components or circuitry.
  • the body-worn device 102 includes hearing assistance components.
  • FIG. 5 is a schematic cross-section view of one embodiment of a body-worn device 200 .
  • the device 200 includes a housing 210 and hearing assistance components 220 disposed within the housing.
  • Hearing assistance components 220 can include any suitable device or devices, e.g., integrated circuits, power sources, microphones, receivers, etc.
  • the components 220 can include a controller 222 (e.g., controller 106 of FIG.
  • the hearing assistance device can be adapted to provide the wearer with auditory information, feedback, or guidance. In one or more embodiments, the hearing assistance device can be adapted to accept verbal or gestural input from the wearer. In one or more embodiments, encephalography (EEG) sensor data can be utilized to infer e.g., the user's intentions.
  • EEG encephalography
  • any suitable controller 222 can be utilized with the body-worn device 200 , e.g., the same controller or controllers described regarding controller 106 of system 100 of FIG. 2 .
  • the controller 222 can be adapted to employ programmable gains to adjust the hearing device output to a patient's particular hearing impairment.
  • the controller 222 can be a digital signal processor (DSP), microprocessor, microcontroller, other digital logic, or combinations thereof.
  • DSP digital signal processor
  • the processing can be done by a single processor or can be distributed over different devices.
  • the processing of signals described herein can be performed using the controller 222 or over different devices.
  • the controller 222 can be operatively connected to but separate from body-worn or ear-worn device.
  • the controller 222 can be in wireless communication with the body-worn device.
  • Raw sensor data, processed sensor data, or characteristics extracted from sensor data can be transmitted to the operatively-connected controller.
  • Data received by the controller can be collected from one or more devices. In some embodiments, redundant data can increase confidence in the data being collected. In some embodiments, sensor data can be rejected if data is not congruent with other devices or if is determined that a particular device is not actually being worn. Aspects of data collection from multiple devices is provided in U.S. patent application Ser. No.
  • data collection may be duty-cycled between operatively connected devices to save resources (battery capacity, wireless bandwidth, processing capabilities, etc.).
  • the controller 222 is adapted to perform instructions stored in one or more memories 232 .
  • Various types of memory can be used, including volatile and nonvolatile forms of memory.
  • the controller 222 or other processing devices execute instructions to perform a number of signal processing tasks.
  • Such embodiments can include analog components in communication with the controller 222 to perform signal processing tasks, such as sound reception by the microphone 224 , or playing of sound using the receiver 226 .
  • digital hearing devices include a controller or processor.
  • programmable gains our audiometric filters can be employed to adjust the hearing device output to a wearer's particular hearing impairment or preferences.
  • the controller 222 (and controller 106 of FIG. 2 ) can be a digital signal processor (DSP), microprocessor, microcontroller, other digital logic, or combinations thereof.
  • DSP digital signal processor
  • the processing can be performed by a single processor or can be distributed over different devices.
  • the processing of signals referenced in this application can be performed using the processor or other different devices.
  • Processing can be done in the digital domain, the analog domain, or combinations thereof.
  • Processing can be done using subband processing techniques. Processing can be done using frequency domain or time domain approaches. Some processing can involve both frequency and time domain aspects.
  • drawings can omit certain blocks that perform frequency synthesis, frequency analysis, analog-to-digital conversion, digital-to-analog conversion, amplification, buffering, and certain types of filtering and processing.
  • the processor is adapted to perform instructions stored in one or more memories, which can or cannot be explicitly shown. Various types of memory can be used, including volatile and nonvolatile forms of memory.
  • the processor or other processing devices execute instructions to perform a number of signal processing tasks. Such embodiments can include analog components in communication with the processor to perform signal processing tasks, such as sound reception by a microphone, or playing of sound using a receiver (i.e., in applications where such transducers are used).
  • different realizations of the block diagrams, circuits, and processes set forth herein can be created by one of skill in the art without departing from the scope of the present subject matter.
  • the hearing assistance components 220 can also include the microphone 224 that is electrically connected to the controller 222 . Although one microphone 224 is depicted, the components 220 can include any suitable number of microphones. Further, the microphone 224 can be disposed in any suitable location within the housing 210 . For example, in one or more embodiments, a port or opening can be formed in the housing 210 , and the microphone 224 can be disposed adjacent the port to receive audio information from the wearer's environment.
  • any suitable microphone 224 can be utilized.
  • the microphone 224 can be selected to detect one or more audio signals and convert such signals to an electrical signal that is provided to the controller 222 .
  • the controller 222 can include an analog-to-digital convertor that converts the electrical signal from the microphone 224 to a digital signal.
  • the receiver 226 Electrically connected to the controller 222 is the receiver 226 . Any suitable receiver can be utilized. In one or more embodiments, the receiver 226 can be adapted to convert an electrical signal from the controller 222 to an acoustic output or sound that can be transmitted from the housing 210 to the wearer. In one or more embodiments, the receiver 226 can be disposed adjacent an opening 212 disposed in a first end 214 of the housing 210 . As used herein, the term “adjacent the opening” means that the receiver 226 is disposed closer to the opening 212 in the first end 214 than to a second end 216 of the housing 210 .
  • the power source 228 is electrically connected to the controller 222 and is adapted to provide electrical energy to the controller and one or more of the other hearing assistance components 220 .
  • the power source 228 can include any suitable power source or power sources, e.g., a battery.
  • the power source 228 can include a rechargeable battery.
  • the components 220 can include two or more power sources 228 .
  • the components 220 can also include the optional antenna 230 . Any suitable antenna or combination of antennas can be utilized.
  • the antenna 230 can include one or more antennas having any suitable configuration. For example, antenna configurations can vary and can be included within the housing 210 or be external to the housing. Further, the antenna 230 can be compatible with any suitable protocol or combination of protocols.
  • the components 220 can also include a transmitter that transmits electromagnetic signals and a radio-frequency receiver that receives electromagnetic signals using any suitable protocol or combination of protocols.
  • the body-worn device 200 (or any other body-worn device described herein) can be connected to one or more external devices using, e.g., Bluetooth, Wi-Fi, magnetic induction, mesh networks, etc.
  • the body-worn device 200 can be wirelessly connected to the Internet using any suitable technique or techniques. Such connection can enable the body-worn device 200 to access any suitable databases, including medical records databases, cloud computing databases, location services, etc.
  • the body-worn device 200 can be wirelessly connected utilizing the Internet of Things (IoT) such that the hearing device can communicate and share data with, e.g., one or more hazard beacons, one or more cameras disposed in proximity to the wearer, motion sensors, room lights, air conditioning and heating controllers, etc. Further, in one or more embodiments, the body-worn device 200 can access weather information via the Internet or a mesh network using any suitable technique or techniques such that the wearer can be informed of potentially hazardous weather conditions.
  • IoT Internet of Things
  • the body-worn device 200 can include the first sensor 234 and the second sensor 236 . Although depicted as including two sensors 234 , 236 , the body-worn device 200 can include any suitable number of sensors, e.g., 1, 2, 3, 4, 5, or more sensors.
  • the sensors 234 , 236 can include any suitable sensor or sensors, e.g., the same sensors described herein regarding sensor 104 of system 100 of FIG. 2 .
  • the first sensor 234 can include the same sensor as the second sensor 236 . In one or more embodiments, the first sensor 234 includes a sensor that is different from that of the second sensor 236 .
  • the sensors 234 , 236 can be operatively connected to the controller 222 using any suitable technique or techniques.
  • first sensor 234 is operatively connected to the body-worn device 200 and adapted to detect a first characteristic of the wearer and generate data (e.g., physiological data or contextual information) representative of the first characteristic.
  • the second sensor 236 is operatively connected to the body-worn device 200 and adapted to detect a second characteristic of the wearer and generate data (e.g., physiological data or contextual information) representative of the second characteristic.
  • the first and second characteristics of the wearer can be any suitable characteristic, e.g., at least one of a physiological characteristic and contextual information of the wearer.
  • the first and second characteristics can include any suitable characteristic, e.g., the same characteristic or characteristics described herein regarding sensor 104 of system 10 of FIG. 4 .
  • the first characteristic detected by the first sensor 234 can be the same as or different from the second characteristic detected by the second sensor 236 .
  • the first characteristic detected by the first sensor 234 can be eye movement of the wearer and the second characteristic detected by the second sensor 236 can be head movement of the wearer.
  • the sensor 104 is operatively coupled to the body-worn device 102 .
  • the sensor 104 can be operatively coupled to the device 102 using any suitable technique or techniques, e.g., electrical, optical, or wireless coupling.
  • the sensor 104 can be disposed in any suitable location.
  • the sensor 104 can be a component of electronic components 220 of the body-worn device 200 , e.g., such as sensors 234 , 236 of electronic components 220 of FIG. 5 .
  • one or more sensors 104 can be disposed outside of the housing of the body-worn device 102 and operatively coupled to the device and the controller 106 using any suitable technique or techniques.
  • one or more sensors 104 can be disposed within one or both ears and outside the ear of the wearer. In one or more embodiments, one or more sensors 104 can be disposed within one or more devices worn by one or more individuals other than the subject of interest. In one or more embodiments, one or more sensors 104 can be disposed within one or more physical hazard beacons, e.g., as described in U.S. Patent Publication No. 2018/0233018 A1, entitled FALL PREDICTION SYSTEM INCLUDING A BEACON AND METHOD OF USING SAME.
  • the sensor 104 can include any suitable sensor or sensors.
  • the sensor 104 can include at least one of an accelerometer, barometer, gyroscope, heart rate sensor, blood pressure sensor, magnetometer, eye sensor, EEG sensor, blood sugar sensor, light sensor, sweat sensor, pupillometry sensor, cerumen sensor, cortisol sensor, body temperature sensor, humidity sensor, air quality sensor, and combinations thereof.
  • the sensor 104 can be adapted to detect any suitable characteristic of the wearer, e.g., at least one of a physiological characteristic and a contextual characteristic of the wearer.
  • the senor 104 can be adapted to detect one or more contextual characteristics proximate to the wearer of the body-worn device 102 .
  • such sensor 104 can include at least one of an ambient temperature sensor, barometer, microphone, GPS sensor, moisture/humidity sensor, image sensor (i.e., a camera), and combinations thereof.
  • the controller 106 Operatively connected to the body-worn device 102 is the controller 106 .
  • the controller 106 can also be operatively connected to the sensor 104 .
  • the controller 106 can include any suitable controller or controllers, e.g., the same controller described regarding controller 222 of the body-worn device 200 of FIG. 5 .
  • the controller 106 can be disposed in any suitable location relative to the body-worn device 102 and the sensor 104 .
  • the controller 106 is disposed within the housing of the body-worn device 102 , e.g., within housing 210 of body-worn device 200 of FIG. 4 .
  • the controller 106 can be disposed external to the body-worn device 102 , e.g., the body-worn device can be wirelessly connected to the wearer's smartphone, computer, or the cloud using any suitable technique or techniques.
  • the controller 106 can include a first controller disposed within the body-worn device 102 and one or more additional controllers disposed externally to the body-worn device.
  • the predictive fall event management system 100 can be utilized to receive input information and determine the likelihood or probability that the wearer of the fall prediction system will fall.
  • the system 100 can be utilized to receive input information from any suitable source to determine whether the wearer has fallen.
  • the input information can be provided using any suitable sensor, device, or database.
  • the input information can be provided to the controller 106 by the sensor 104 , the body-worn device 102 , manually by one or more of the wearer, a caregiver, and a medical professional, or obtained from other systems via wired or wireless connections to system 100 and/or to cloud-based data server.
  • the predictive fall event management system 100 can provide any suitable outputs that can be based on the probability of a fall or that a fall has occurred. Any suitable output or outputs can be provided by the system 100 , e.g., notifications, reports, IoT triggers (e.g., activating room lighting), treatments to the wearer of the device 102 , etc.
  • the system 100 can be utilized to detect head impact, check with the wearer for consciousness, and inform one or more of the wearer, caregiver, and medical professional of the detection of a head impact and level of consciousness of the wearer.
  • the fall prediction system 100 can utilize any suitable technique or techniques to determine the risk of a fall and/or that a fall has occurred.
  • FIG. 6 is a flowchart of one embodiment of method 300 for predicting and detecting a fall.
  • the method 300 can be implemented using any suitable device or system, e.g., predictive fall event management system 100 of FIG. 3 . Although described in reference to predictive fall event management system 100 of FIG. 3 , the method 300 can be utilized with any suitable device or system.
  • controller 106 system 100 of FIG. 3 can be adapted to perform method 300 . Further, the controller 106 can be adapted to perform the method 300 through firmware, software, etc. In one or more embodiments, the controller 106 can be or include an application-specific integrated circuit (ASIC) that includes the method 300 .
  • ASIC application-specific integrated circuit
  • Method 300 includes receiving inputs 302 at 304 for data preparation. Any suitable inputs 302 can be received.
  • inputs 302 can include physiological data representative of a physiological characteristic of the wearer of the body-worn device 102 over a monitoring time period. Further, inputs 302 can include contextual data representative of context information of the wearer over the monitoring time period. Medical/health reports regarding the wearer can also be provided as inputs 302 .
  • Any suitable databases containing, e.g., calendar data, reminder data, and data relative to actions of an artificial intelligence can also be provided as inputs 302 . Further, group data from the cloud can also be provided as inputs at 302 .
  • data from inputs 302 can be, e.g., filtered, time stamped, assigned metadata memory locations, and combinations thereof, at 304 .
  • data can include a single datum or a plurality of data values or statistics.
  • statistics can include any appropriate mathematical calculation or metric relative to data interpretation, e.g., probability, confidence interval, distribution, range, or the like.
  • one or more physiological risk factor models can receive the physiological data from inputs 302 and provide to storage 308 one or more outputs of risk factor values or statistics of a fall by the wearer based on the physiological data.
  • the physiological risk factor models can utilize any suitable technique or techniques to determine physiological risk factor values or statistics at 306 .
  • contextual data inputs 302 can be received by contextual factor models at 310 .
  • the contextual factor models 310 can provide to storage 312 one or more outputs of contextual risk factor values or statistics of a fall by the wearer based upon contextual data.
  • the contextual factor models 310 can utilize any suitable technique or techniques to provide contextual factor values or statistics.
  • one or more future physiological states or contextual states based at least in part one or more of the physiological data and contextual data can also be determined at 306 and 310 , and then stored at 308 and 312 respectively. Any suitable technique or techniques can be utilized to determine the future physiological states and future contextual states.
  • One or more pattern recognition machine learning techniques such as Hidden Markov Models (HMMs), Bayesian models, non-linear regressions, decision trees, support vector machines, neural networks, etc. can be applied to input data 302 to predict future physiological and contextual states based upon, at least in part, one or more of present and historical data.
  • prepopulated group data or laboratory data can be utilized to form the basis of training one or more pre-trained models. As the system collects data about the user over time, one or more of these models can be updated using any suitable training techniques.
  • a particular model at 306 can be trained or continuously adapted at 314 to learn the effects that a specific medication has on an individual, over time, based on data from a group of individuals.
  • the model can be further updated, for a specific user, as specific data relative to the severity and timing of any observable changes to the user is collected.
  • an observable change can relate to one or more characteristics of the user's gait and postural stability that can be observed or monitored using any suitable sensor arrangement.
  • the time at which the user will ingest a particular medication can be predicted based upon any suitable contextual data e.g., historical, physiological, or historical contextual data, scheduled reminders, push notifications, location information, etc.
  • a particular model at 310 can be trained or continuously adapted at 316 based on an individual's historical environment and location data, over time, to recognize patterns such that a prediction of the individual's future location can be made at 310 .
  • Historical fall event and balance event data from one or more of the individual or a group of individuals, at a given location, can further inform the risk factor associated with the location the individual is predicted to be at in a future time.
  • the physiological risk factor models 306 can be updated at 314 based upon one or more of the inputs 302 and the physiological risk factor values or statistics. For example, changes in gait such as increased postural sway, irregular stride, lower foot clearance, and decreased walking speed can be detected, using any suitable means, and can indicate that a new lower limb limitation can increase the individual's risk for stumbling over uneven walking paths.
  • the contextual factor models 310 can also be updated at 316 based upon one or more of the inputs 302 and the contextual risk factor values or statistics. For example, changes in weather conditions or geographical locations of the wearer can be factored into the contextual factor models 310 using any suitable technique or techniques.
  • the method 300 includes receiving balance data representative of a balance event and updating a fall risk model based upon one or more of the physiological data, contextual data, and the balance data.
  • the balance data can be an input 302 and provided as an output from the signal/data preparation at 304 to the physiological factor models at 306 .
  • Balance events can include a vestibular disturbance of the wearer, an occurrence of a fall, an episode of dizziness, an episode of syncope, a seizure, a stroke, an anaphylactic shock, an aneurysm, a hard sit into a chair, a near fall, a trip, a stumble, a change in gait, a fear of falling, or the like of the wearer of the body-worn device 102 .
  • balance, fall, and positional data can also be utilized at 314 to update the physiological factor models at 314 .
  • the controller 106 can be adapted to detect disruption to an individual's postural stability, e.g., a fall or other balance event (temporary instability) using any suitable technique or techniques.
  • At 318 at least one of the physiological risk factor statistics and contextual risk factor statistics can be provided to one or more fall risk prediction sub-models at 318 .
  • One or more fall risk statistics could be provided for one or more future moments in time. Any suitable technique or techniques can be utilized with the one or more fall risk prediction sub-models 318 .
  • multiple fall risk statistics models can be produced at 318 that is contingent upon the occurrence of one or more predicted contextual factors. For example, the individual's fall risk statistics can be different if they decided to either stand up or remain seated. In one or more embodiments, the individual can be informed regarding one or more predicted fall risk statistics such that the individual understands the predicted consequences of one or more predicted actions that they may take.
  • sub-models 318 can be adapted to determine a future fall risk value based upon the one or more future physiological states or contextual states using any suitable technique or techniques.
  • the sub-model can be further trained or continuously adapted based on the historical fall event and balance event data from one or more of the individual or a group of individuals.
  • the sub-models 318 can generate a risk or probability value of a fall that can be based on a predetermined formula or formulas that can be derived from experimental data.
  • the formula can also be entirely learned or modified through various machine learning approaches.
  • the method 300 can send postural data collected before the event by one or more sensors 104 , e.g., to a cloud server.
  • data from the wearer and other wearers can be used to train a regression model or deep neural network to estimate the risk of a fall for an individual wearer at 318 .
  • One or more of the sub-models 318 can be generated from analytics or machine learning of larger group data using any suitable technique or techniques, e.g., regression, steady-state, Bayesian, classification trees, Volterra, support vector machine, Gaussian mixture, neural network techniques, and combinations thereof.
  • suitable technique or techniques e.g., regression, steady-state, Bayesian, classification trees, Volterra, support vector machine, Gaussian mixture, neural network techniques, and combinations thereof.
  • the sub-models 318 can provide an estimate or probability of the general risk (or capability of keeping balance) of the wearer and learn the wearer's norms regarding motion patterns and health/physiological information.
  • Inputs for generating the sub-models 318 can either be obtained based on clinical evaluations and medical history or be learned by the predictive fall event management system 100 from one or more inputs provided by various types of sensors, e.g., sensor 104 and responses to queries.
  • motion patterns of the wearer and changes to such patterns can be estimated and monitored based on the outputs from one or more of an inertial measurement unit (IMU) sensor, GPS sensor barometer, magnetometer, EEG sensor, camera, etc.
  • IMU inertial measurement unit
  • the motion of the wearer can include sway amplitude and speed while walking, speed and trajectory when sitting down or standing up, speed and radius when turning, stride length, symmetry and variance, frequency of walks, length or distance of walks, reaction speed, etc.
  • one or more of periodic assessments of functional balance ability, muscle strength, perceived balance efficacy, fear of falling, or functional reaction speed of the user can be performed by the system 100 and queried to the user or a third party.
  • one or more of results of periodic functional balance ability, muscle strength, perceived balance efficacy, fear of falling, or functional reaction speed of the user can be entered into the system 100 manually by either the user or a third party.
  • the results of e.g., the Timed Up and Go (TUG) test can be calculated either by the instrumented body-worn device or entered by the individual or a third-party observer.
  • physiological data that can be provided as inputs to the sub-models 318 include heart rate, blood pressure, blood sugar, blood oxygen, core body temperature, etc., and can be monitored utilizing any suitable sensor or sensors 104 . All such inputs and how they change over time can be monitored and used to estimate whether a fall condition is satisfied (i.e., how prone the wearer is to a fall).
  • one of the sub-models 318 can evaluate postural stability (i.e., displacement of the head of the wearer in three dimensions) of the wearer to determine a fall risk value based on monitoring of the wearer at 306 .
  • Any suitable sensor or sensors 104 can be utilized to determine postural stability, e.g., one or more of an accelerometer, gyroscope, microphone, barometer, optical sensor, and bioelectrical sensor.
  • the sensor 104 can include an accelerometer and a gyroscope as the primary sensors for postural balance and fall-risk monitoring and the other sensors can be secondary sensors.
  • a secondary sensor can include a microphone that can be used for detecting foot-falls or a fall event.
  • a barometer can be used to detect stair climbing.
  • an optical sensor can be used for measuring heart rate and other biosignals.
  • a bioelectric sensor can be used for monitoring electro-, cardio-, encephalo-, occulo-, and myo-graph signals from any location on the head and body of the wearer.
  • One or more physiological sensors 104 can be employed to identify a “prodrome” of a postural instability. Some possible techniques of using this sensor information for this purpose can be used individually or in combination.
  • the senor 104 can include one or more of an accelerometer and a gyroscope. Signals form the sensor 104 can be used to compute and monitor a deviation from a stable position and a velocity with which that takes place.
  • the controller 106 can utilize the signal inputs from the sensor 104 to generate a measure of postural stability.
  • postural stability can be included in one or more physiological data inputs at 302 .
  • Postural stability can be recorded during normal daily activities, including standing, walking, and climbing stairs.
  • Postural stability can also be recorded during structed activities and functional tests, e.g., during performance of a TUG test or the like.
  • a threshold of normal stability can be established based on clinical postural stability testing or during a user-initiated initialization involving one or more of these activities. Measurements in case of a recorded fall can be used to adjust the threshold, if appropriate.
  • Acceleration of the head of the wearer while walking is complex, with the most prominent feature in the unprocessed accelerometer signal being that of the footfall. Adding to this complexity can be stabilization of the head by the neck. Footfall signals can be diminished by neck stabilization but still can be detectable. Vestibular-ocular reflexes can also be measured as the eye will attempt to stabilize the individual's visual field with each step.
  • head oscillation in three dimensions AP
  • lateral, and vertical can measured. Components of the displacement and the velocity in each dimension can be computed as measures of the postural stability. Although generally correlated and constrained by the body, the head can move relatively independently, which introduces artifacts.
  • the velocity and displacement of the head oscillation are computed only when the pitch, yaw and/or roll motions of the head a slower than some predefined thresholds.
  • Artifacts related to head movements can also be mitigated, by the controller, through the integration of sensor inputs of body-worn sensors placed on the chest, trunk, waist, etc. The values or statistics can depend upon the speed and type of body movement.
  • the controller 106 can be adapted to determine a fall condition by measuring a maximum displacement between a longitudinal axis of the wearer and a normal to the earth's surface as a function of time. Further, in one or more embodiments, the controller 106 can be adapted to determine the fall condition by measuring a maximum velocity of displacement between a longitudinal axis of the wearer and a normal to the earth's surface.
  • Fall risk thresholds related to safe postural stability or limits of stability can be established by balance testing in a clinical setting or by user-conducted or self-directed tests.
  • a fall risk signal or other fall risk output can be generated based on single or multiple threshold crossings.
  • Parameters of postural stability i.e., balance metrics, and fall risk values or statistics can be of interest to one or more of the wearer, caregivers such as the family members, and medical professionals.
  • Balance metrics and fall risk values or statistics can be monitored e.g., daily or hourly and transmitted to various parties.
  • the system can continuously monitor the user and once a fall risk threshold is exceeded, a fall risk output such as a discrete audio alert can be provided to the user.
  • head worn IMU sensors can be utilized to characterize small motions (e.g., sway) that can be important for balance evaluation.
  • the orientation of the IMU sensors is highly controlled and well calibrated in the laboratory.
  • Any suitable technique or techniques can be utilized to align the sensor 104 in both left and right hearing devices of the system 10 , e.g., the techniques described in U.S. patent application Ser. No. 15/331,230, filed Oct. 21, 2016, and entitled HEAD RELATED TRANSFER FUNCTION INDIVIDUALIZATION FOR HEARING DEVICE.
  • a technique can be utilized to compensate for the orientation mismatch between two hearing devices so that the IMU sensors on both sides of the head can be collaboratively aligned with the head orientation and used to derive postural stability information.
  • the fall risk value based upon postural stability can be determined by first detecting that the wearer is walking. One or more artifacts from the sensor 104 caused by foot-impact can be filtered out using any suitable technique or techniques.
  • Postural stability can be determined using any suitable technique or techniques. Velocity components of such postural stability can be determined using any suitable technique or techniques.
  • the fall risk value can be based upon walking speed, distance walked, frequency of walks, duration of walks, frequency of successful postural transitions, speed of postural transitions, or the like and other activity classifications, and combinations thereof.
  • a composite sensitivity parameter of the contribution of the sensor 104 (e.g., one or more accelerometers) to the overall fall risk value or statistic can be determined using any suitable technique or techniques.
  • the sensitivity of the fall risk value or statistics to an amplitude of the postural stability can be determined using, e.g., one or more of a user input after a near-fall event, a balance study, and fall detection.
  • the sensitivity of the fall risk value or statistics to the stability velocity at a pre-determined postural stability can be determined using, e.g., one or more user inputs after a near-fall event, a balance test, fall detection or the like. Further, the sensitivity of the fall risk value to a statistically determined combination of the postural stability and the stability velocity can also be determined.
  • postural stability, sway velocity and other posture, walking and fall-related information can be routinely transmitted to healthcare professionals.
  • the wearer's posture while standing and walking, actual fall events, and user-indicated near-fall events can also be transmitted to healthcare professionals.
  • an alert can be sent to one or more of the wearer, caregiver, and medical professional.
  • Such alerts can include instructions for how to prevent a fall from occurring.
  • sensors 104 having one or more accelerometers can be placed in both ears of the wearer. Acceleration of the mid-point between the two ears, as opposed to that of one ear, can be calculated to determine postural stability. Further, false positives of fall detection can be reduced by ensuring both sensors 104 follow the same nominal motion pattern. In addition, head rotation around the vertical axis i.e., the yaw, can also be determined and utilized to calculate the fall risk value.
  • a fall condition can be determined by measuring eye movement of the wearer.
  • the fall prediction system 100 can detect eye movements and compare such eye movements to a baseline to determine whether a vestibular event is occurring that can increase the risk of fall.
  • the sensor 104 of the predictive fall event management system 100 can include one or more eye movement sensors.
  • the system 100 can also include one or more sensors 104 that can measure head movement of the wearer. Data from such head movement sensors 104 can be utilized to correlate with eye movement sensor data to determine the risk of a fall.
  • Any suitable fall prediction system or device can be utilized to measure eye movement of a wearer, e.g., the devices described in U.S. Pat. No. 9,167,356, issued Oct. 20, 2015, and entitled ELECTROOCULOGRAM AS A CONTROL IN A HEARING ASSISTANCE DEVICE.
  • one or more inputs can be provided by one or more of the wearer, the caregiver, and the physician.
  • one or more inputs can be provided by the wearer in response to one or more queries provided, e.g., by the body-worn device 102 (or ear-worn device or hearing device), the caregiver, or the physician.
  • one or more of the sub-models 318 can evaluate eye movement of the wearer to determine a fall risk value based on monitoring of the wearer at 306 .
  • the system 100 can detect eye movements and compare such eye movements to a baseline to determine whether a vestibular event is occurring that can increase the risk of fall.
  • the sensor 104 of the fall prediction system 100 can include one or more eye movement sensors.
  • the system 100 can also include one or more sensors 104 that can measure head movement of the wearer. Data from such head movement sensors 104 can be utilized to correlate with eye movement sensor data to determine the risk of a fall. Any suitable system or device can be utilized to measure eye movement of a wearer, e.g., the devices described in U.S. Pat. No. 9,167,356, issued Oct. 20, 2015, and entitled ELECTROOCULOGRAM AS A CONTROL IN A HEARING ASSISTANCE DEVICE.
  • data from eye movement sensors e.g., Electrooculography (EOG) sensors
  • positional sensors collectively sensors 104 of FIG. 4
  • eye movement sensors can be utilized for early detection of peripheral vestibular asymmetry (which generally cause nystagmus and feelings of imbalance/dizziness to occur).
  • Nystagmus is an involuntary oscillation of one or both eyes about one or more axes.
  • the eye movement sensors can allow the system 100 to make one or more of the following determinations: (a) whether or not the nystagmus is typical given the wearer's baseline movement data, (b) whether the wearer's visual pursuits are smooth or otherwise atypical.
  • Other sensors can be utilized with the method 300 to predict and/or detect falls.
  • the system 100 can include at least one of a microphone and an ambient light sensor. Any suitable technique or techniques can be utilized to determine a fall risk value utilizing eye movement sensors, e.g., the techniques described in U.S. Patent Publication No. 2018/0228404 A1, entitled FALL PREDICTION SYSTEM AND METHOD OF USING SAME.
  • Outputs from the fall risk prediction sub-models 318 can be stored at 320 . Such outputs can include data that includes risk-level scaling and trends.
  • a determination, for a future time, of whether a fall condition is satisfied can be made based upon the one or more future physiological states or context states. Any suitable technique or techniques can be utilized at 322 to determine whether a fall condition has been satisfied.
  • reinforcement learning techniques at 324 can be applied to select the best possible output behavior strategy in a given situation. Reinforcement learning models can consider one or more of a predicted physiological risk factor, predicted contextual risk factor, historical physiological risk factor, historical contextual factor, predicted fall event, historical fall event, predicted balance event, historical balance events, and the like.
  • the reinforcement learning model can further consider the fall risk statistics associated with historical data to optimize one or more of the present and future fall prevention outputs at 324 .
  • determining whether the fall condition is satisfied at 322 includes applying a future fall risk value to a fall risk model.
  • Such fall risk model can be developed from outputs from the fall risk prediction sub-models 318 .
  • the system 100 can include several models: a physiological model, a contextual model, and a fall risk model.
  • the fall risk model can use the output (e.g., state) of the physical model and contextual model as inputs to the fall risk model.
  • the future fall risk value can be based upon the one or more future physiological states or contextual states.
  • the controller 106 can be adapted to determine at 322 , for each of a plurality of future times, whether an anticipated fall condition is satisfied based upon one or more of the physiological data and contextual data. Further, in one or more embodiments, the controller 106 can be adapted to determine at 322 , for a plurality of future times, whether an anticipated fall condition is satisfied based upon a combination of the physiological data and the contextual data. In one or more embodiments, the controller 106 can be adapted to determine at 322 , for a plurality of future times, whether the fall condition is satisfied by determining a future fall risk value based upon the one or more future physiological states or contextual states.
  • Data related to fall conditions including thresholds can be stored at 326 and utilized at 322 to determine whether the fall condition has been satisfied at 322 and also utilized to generate fall prevention outputs at 324 .
  • the fall prevention output can include one or more outputs described herein.
  • the fall prevention output can include one or more alerts that are provided to one or more of the wearer, caregiver, and medical professional for proper diagnosis and treatment at 328 .
  • a fall prevention output can include initiating mitigation therapy at 330 .
  • Such mitigation therapy can include initiating a balance training regimen or corrective measure as described, e.g., in U.S. Patent Publication No. 2018/0317837A1, entitled HEARING ASSISTANCE DEVICE INCORPORATING VIRTUAL AUDIO INTERFACE FOR THERAPY GUIDANCE.
  • the fall prevention output can include delivery or modification of a therapy or initiation of an intervention, e.g., activation of an exoskeleton worn by the wearer. Further, the fall prevention output can include generating an IoT action at 332 such as sending one or more signals to one or more IoT devices proximate to the wearer to help prevent the fall or otherwise protect the wearer from injury, increasing an intensity of the ambient light for the wearer's environment, etc.
  • a particular instance of when the fall condition is satisfied at 322 and which fall prevention outputs are generated at 324 can be stored at 334 .
  • Such data that is stored at 334 can be utilized to updated one or both of the physiological factor models at 314 or the contextual factor models at 316 . Further, such data of a fall condition being met can be utilized to modify one or more of the fall risk prediction sub-models at 318 .
  • the method 300 proceeds to a wait condition at 336 .
  • a predictive fall event management system having a body-worn device, and a controller operatively connected to the body-worn device, wherein the controller is adapted to: receive one or more of physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period, and contextual data representative of context information of the wearer over the monitoring time period, determine one or more future physiological states or contextual states based at least in part on one or more of the physiological data and the contextual data, determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states, and generate a fall prevention output responsive to satisfaction of the fall condition.
  • the system further can include a sensor operatively connected to the body-worn device and adapted to detect the physiological characteristic of the wearer of the body-worn device and generate the physiological data representative of the physiological characteristic, wherein the controller is adapted to receive the physiological data from the sensor.
  • the controller is further adapted to determine, for each of a plurality of future times, whether an anticipated fall condition is satisfied based upon one or more of the physiological data and the contextual data.
  • the controller is further adapted to determine, for a plurality of future times, whether an anticipated fall condition is satisfied based upon a combination of the physiological data and the contextual data.
  • the controller is further adapted to determine, for a future time, whether the fall condition is satisfied by determining one or more of a future fall risk value and statistics based upon the one or more future physiological states or contextual states.
  • determining whether the fall condition is satisfied includes applying one or more of the fall risk value and statistics to a fall risk model.
  • the controller is further adapted to: receive balance data representative of a balance event, and update the fall risk model based upon one or more of the physiological data, contextual data, and the balance data.
  • physiological data includes at least one of a determined fall risk, inertial sensor data, heart rate information, blood pressure information, drug concentration information, blood sugar level, body hydration information, neuropathy information, blood oximetry information, hematocrit information, body temperature, cortisol levels, age, sex, gait or postural stability attribute, vision, eye movement, or head movement.
  • physiological data includes one or more inputs provided by the wearer in response to one or more queries.
  • contextual data includes at least one of weather condition, environmental condition, sensed condition, location, velocity, acceleration, direction, hazard beacon, type of establishment occupied by the wearer, camera information, or presence of stairs.
  • the fall prevention output includes one or more of an alert provided to the wearer, electrical stimulation, and thermal stimulation, wherein the alert includes one or more of an audible alert, a visual alert, or a tactile alert.
  • the fall prevention output includes a modification of an environmental context of the wearer.
  • the fall prevention output includes transmission of one or more of the physiological data and the contextual data to one or more of a caregiver, a medical professional, a database, or the wearer.
  • the controller is further adapted to detect disruption to the wearer's postural stability.
  • a method including receiving one or more of physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period, and contextual data representative of context information of the wearer over the monitoring time period, determining one or more future physiological states or contextual states based at least in part on one or more of the physiological data or the contextual data, determining, for a future time, whether a fall condition is satisfied based upon one the one or more future physiological states or contextual states, and generating a fall prevention output responsive to satisfaction of the fall condition.
  • a method further can include determining, for a plurality of future times, whether an anticipated fall condition is satisfied based upon one or more of the physiological data and the contextual data.
  • a method further can include determining, for a plurality of future times, whether an anticipated fall condition is satisfied based upon a combination of the physiological data and the contextual data.
  • a method further can include: receiving balance data representative of a balance event, and updating the fall risk model based upon one or more of the physiological data, contextual data, and the balance data.
  • determining whether a fall condition is satisfied includes applying one or more of the fall risk value and statistics to a fall risk model.
  • determining whether a fall condition is satisfied includes determining one or more of a future fall risk value and statistics based upon the one or more future physiological states or contextual states.
  • phrases “at least one of” and “comprises at least one of” followed by a list refers to any one of the items in the list and any combination of two or more items in the list.
  • the term “about” refers to that variation in the measured quantity as would be expected by the skilled artisan making the measurement and exercising a level of care commensurate with the objective of the measurement and the precision of the measuring equipment used.
  • “up to” a number includes the number (e.g., 50).
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